Recovery Guarantees for Rank Aware Pursuits

J.D. Blanchard, M.E. Davies

Research output: Contribution to journalArticlepeer-review

Abstract

This letter considers sufficient conditions for sparse recovery in the sparse multiple measurement vector (MMV) problem for some recently proposed rank aware greedy algorithms. Specifically we consider the compressed sensing framework with Gaussian random measurement matrices and show that the rank of the measurement matrix in the noiseless sparse MMV problem allows such algorithms to reduce the effect of the log n term that is present in traditional OMP recovery.
Original languageEnglish
Pages (from-to)427-430
Number of pages4
JournalIEEE Signal Processing Letters
Volume19
Issue number7
DOIs
Publication statusPublished - 1 Jan 2012

Keywords / Materials (for Non-textual outputs)

  • Greedy algorithm
  • multiple measurement vectors
  • orthogonal matching pursuit
  • rank

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